PhD: Safe Cooperation of Autonomous Vehicles in Mixed Traffic

Research / Academic

Autonomous vehicles (such as cars and vessels) will be widespread in our daily lives, aiming at reducing pollution while improving traffic efficiency and safety. The ability of these vehicles to cooperate in planning trajectories is one of the main strengths of this technology. The presence of human-operated vehicles and the occurrence of sensor/actuator faults, however, complicate the vehicle cooperation. Failing to handle these mixed-traffic uncertainties and faults in the motion planning strategy can inevitably compromise the cooperation. The goal of this project (SCoop) is to design a cooperation framework to allow autonomous vehicles to safely navigate in the presence of human-operated vehicles and faults. To design a novel safe cooperation framework, the project will rely on tools for uncertainty estimation/fault diagnosis and distributed motion planning. Experiments on real autonomous surface vessels (ASVs) will demonstrate the effectiveness of the proposed design. SCoop is a Cohesion project between the Cognitive Robotics Department and the Maritime and Transport Technology Department.

In this project, you as a PhD candidate will investigate how to design a motion planner for an autonomous vehicle that: (1) deals with mixed-traffic uncertainties and faults to avoid collisions and deadlocks by discriminating between local and propagated faults; (2) estimates these uncertainties and faults in the local motion planning; (3) combines the design of the undertainty estimation, fault detection and isolation and local motion planning. The multi-vehicle coordination methods that will be developed in this project will be tested and verified through their application to autonomous vessels in the ResearchLab Autonomous Shipping (RAS).


The candidate has a very good MSc degree in Systems and Control, Electrical/Mechanical Engineering, Applied Mathematics, or a related field. The candidate must have strong analytical skills and must be able to work at the intersection of several research domains. Good programming skills and experience with MATLAB and ROS are of foremost importance to implement the proposed designs on real ASVs. A very good command of the English language is required, as well as excellent communication skills. Candidates having exhibited their ability to perform research in control, optimization, system identification, and/or robotics are especially encouraged to apply.

Salary Benefits:

TU Delft offers a customisable compensation package, a discount for health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. An International Children’s Centre offers childcare and an international primary school. Dual Career Services offers support to accompanying partners. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
As a PhD candidate you will be enrolled in the TU Delft Graduate School. TU Delft Graduate School provides an inspiring research environment; an excellent team of supervisors, academic staff and a mentor; and a Doctoral Education Programme aimed at developing your transferable, discipline-related and research skills. Please visit for more information.

Work Hours:

38 hours per week


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